Category: Interview

DeepMind’s Research Platform Team has open-sourced TF-Replicator, a framework that enables researchers without previous experience with the distributed system to deploy their TensorFlow models on GPUs and Cloud TPUs. The move aims to strengthen AI research and development.

Model-free reinforcement learning can be used to learn effective strategies for complex tasks such as Atari games, but it usually requires a large amount of interaction, which adds significant time and cost.

Natural language processing has made significant progress in the past year, but few frameworks focus directly on NLP or sequence modeling. Google Brain recently released Lingvo, a deep learning framework based on TensorFlow. Synced invited Ni Lao, Chief Science Officer at Mosaix, to share his thoughts on Lingvo.

Machine learning models based on deep neural networks have achieved unprecedented performance on many tasks. These models are generally considered to be complex systems and difficult to analyze theoretically. Also, since it’s usually a high-dimensional non-convex loss surface which governs the optimization process, it is very challenging to describe the gradient-based dynamics of these models during training.

Last Monday US President Donald Trump signed the “American AI Initiative,” an executive order designed to spur US investment in artificial intelligence and boost the domestic AI industry. The initiative has five highlights: Investing in AI Research and Development (R&D), Unleashing AI Resources, Setting AI Governance Standards, Building the AI Workforce, International Engagement and Protecting our AI Advantage.

Synced is proud to present Gary Marcus as the last installment in our Lunar New Year Project — a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. (Read the previous articles on Clarifai CEO Matt Zeiler and Google Brain Researcher Quoc Le.)

The Synced Lunar New Year Project is a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this second installment (click here to read the previous article on Clarifai CEO Matt Zeiler), Synced speaks with Google Brain Researcher Quoc Le on his latest invention, AutoML, Google Brain’s pursuit of AI, and the secret of transforming lab technologies into real practices.

Uber AI Lab has created a buzz in the machine learning community with the publication of a paper introducing a new reinforcement learning algorithm called Go-Explore. The algorithm is designed to overcome the challenges of intelligence exploration in reinforcement learning to improve performance on hard-exploration tasks.

This is the first installment of the Synced Lunar New Year Project, a series of interviews with AI experts reflecting on AI development in 2018 and looking ahead to 2019. In this article, Synced chats with Clarifai Founder and CEO Matt Zeiler on recent progress in computer vision and his company’s plans for the future. Founded in New York in 2013, Clarifai produces advanced image recognition systems.

In an exclusive interview with Synced at NeurIPS, members of the University of Toronto and Vector Institute team led by Assistant Professor David Duvenaud discussed their winning submission Neural Ordinary Differential Equations — a math-based approach to designing deep learning models that is stimulating discussion across the machine learning community.

A founding member of Google Brain and the mind behind AutoML, Quoc Le is an AI natural: he loves machine learning and loves automating things. Le used millions of YouTube thumbnails to develop an unsupervised learning system that recognized cats when he was a Stanford University PhD in 2011.

As Chinese Internet giant Baidu has expanded from search to mobile apps, cloud services, and emerging business sectors like autonomous driving and voice assistants, it has correspondingly beefed up its research efforts, particularly in AI, to keep pace with growing security threats.

Robert S. Warren, MD is a Professor of Surgery and a specialist in gastrointestinal and liver cancer. Dr. Warren joined UCSF Medical Center in 1988. Highly respected by his peers, Dr. Warren was named to the list of U.S. News “America’s Top Doctors,” a distinction reserved for the top 1% of physicians in the nation for a given specialty.

MORE Health is a Silicon Valley-based company that provides access to top international physicians for patients faced with critical illnesses such as cancer or heart disease. The company was founded in 2013, and recently took a leap forward by partnering with Houston-based Melax Technologies…

Liulishuo is the AI English teacher on your phone. You don’t need to know how it works, yet it helps you learn English more efficiently than a human teacher,” says Yi Wang, Founder and CEO of Liulishuo — a Beijing-based “AI + language” company…

Mobvoi hired Dr. Mei-Yuh Hwang as its Vice President of Engineering in 2016. A respected speech recognition researcher and former Principle NLP Scientist Manager at Microsoft, Dr. Hwang is a pioneer in voice recognition and machine translation.

We interviewed Mr. Qiang Dong, chief scientist of Beijing YuZhi Language Understanding Technology Co. Ltd. and learned more about their YuZhi NLU Platform that conducts its unique semantic analysis based on concepts rather than words.

“To pretend science is morally neutral, and that you can develop anything and have clean hands because it is someone else using it to kill people is extremely naive, and scientists should know that.” – UC Berkeley’s Dr. Stuart Russell

Personal computers and mobile devices are in their heyday. Researchers are swarming standalone AI, focusing on how to automate self-learning intelligent systems. The interfaces for wearables meanwhile are evolving from smart screens to gesture commands, like those often seen in AR and VR commercials.

Professor Richard Sutton is considered to be one of the founding fathers of modern computational reinforcement learning. He made several significant contributions to the field, including temporal difference learning, policy gradient methods, and the Dyna architecture.